ac

Utilization of Data Analytics to Enhance Operational Efficiency in Manufacturing Companies

Authors

  • Rendi Aprijal Universitas Pembangunan Panca Budi
  • Iqbal Wiranata Siregar Universitas Pembangunan Panca Budi
  • Andysah Putera Utama Siahaan Universitas Pembangunan Panca Budi
  • Leni Marlina Universitas Pembangunan Panca Budi

DOI:

10.47709/cnahpc.v6i2.3723

Keywords:

Big Data Analytics, Manufacturing Efficiency, Operational Optimization, Operational Challenges, Data-driven Decision Making

Dimension Badge Record



Abstract

In the digital era, manufacturing industries confront challenges like heightened global competition and intricate production processes, urging them to boost efficiency and productivity. Amidst these circumstances, Big Data emerges as a pivotal opportunity to enhance manufacturing performance. Big Data, characterized by vast volumes of data, utilizes advanced data mining to machine learning techniques for analysis. Data analytics, an interdisciplinary field, profoundly impacts manufacturing operations, enabling deeper insights into production processes. By analyzing production data, companies identify inefficiencies, streamline workflows, and enhance operational efficiency and productivity. Predictive maintenance through sensor data analysis prevents machine failures, while logistics data analysis optimizes supply chains and inventory management, reducing costs and enhancing competitiveness. However, implementing Big Data analytics presents challenges such as rapid data growth, diverse data sources, real-time insights, skill shortages, and data fragmentation. Overcoming these hurdles requires robust technology, skilled personnel, and effective data management strategies. Examples of Big Data analytics applications include customer behavior analysis by Amazon and Netflix, fraud detection in insurance, and urban mobility optimization. Success factors in data analytics implementation include effective data-driven communication, technology integration, and skill enhancement. In conclusion, implementing Big Data Analytics in manufacturing promises significant benefits in operational efficiency, product quality, and competitiveness. Overcoming challenges necessitates robust strategies and consideration of ethical and security issues, ensuring responsible data usage. With a deep understanding of Big Data Analytics, manufacturing companies can leverage this technology to achieve higher efficiency and competitiveness in the global market.

Downloads

Download data is not yet available.
Google Scholar Cite Analysis
Abstract viewed = 81 times

References

Abdul-Jabbar, S. S., & K. Farhan, A. (2022). Data Analytics and Techniques. Aro-the Scientific Journal of Koya University, 10(2), 45–55. https://doi.org/10.14500/aro.10975

Abuali, M. (2013). Big data in manufacturing. Proceedings for the Joint Conference: MFPT 2013 and ISA’s 59th International Instrumentation Symposium, ISA 2013: Sensors and Systems for Reliability, Safety and Affordability, (March). https://doi.org/10.51542/ijscia.v2i1.11

Alsolbi, I., Shavaki, F. H., Agarwal, R., Bharathy, G. K., Prakash, S., & Prasad, M. (2023). Big data optimisation and management in supply chain management: a systematic literature review. Artificial Intelligence Review, 56, 253–284. https://doi.org/10.1007/s10462-023-10505-4

Aniko, A. R., Muttaqin, A. N., & Fadilah, M. I. (2024). Business Process Management in IT Company: Systematic Literature Review. SITEKNIK?: Sistem Informasi, Teknik Dan Ilmu Terapan, 1(1), 58–67. Retrieved from https://jurnalsiteknik.com/index.php/halaman/article/view/8/7

Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2020). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 153, 104559. https://doi.org/10.1016/j.resconrec.2019.104559

Balaji, D. . (2022). To examine the application of data science to physics. Technoarete Transactions on Advances in Data Science and Analytics, 1(2), 21–28. https://doi.org/10.36647/ttadsa/01.02.a004

Chang, V. I. C., & Lin, W. (2018). How Big Data Transforms Manufacturing Industry. International Journal of Strategic Engineering, 2(1), 39–51. https://doi.org/10.4018/ijose.2019010104

Dahiya, R., Le, S., Ring, J. K., & Watson, K. (2022). Big data analytics and competitive advantage: the strategic role of firm-specific knowledge. Journal of Strategy and Management, 15(2), 175–193. https://doi.org/10.1108/JSMA-08-2020-0203

Dai, H. N., Wang, H., Xu, G., Wan, J., & Imran, M. (2020). Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies. Enterprise Information Systems, 14(9–10), 1279–1303. https://doi.org/10.1080/17517575.2019.1633689

Dasgupta, N. (2018). Practical Big Data Analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R. Packt Publishing.

Fang, P., Yang, J., Zheng, L., Zhong, R. Y., & Jiang, Y. (2020). Data analytics-enable production visibility for Cyber-Physical Production Systems. Journal of Manufacturing Systems, 57(November), 242–253. https://doi.org/10.1016/j.jmsy.2020.09.002

Hajmirfattahtabrizi, M., & Song, H. (2019). Investigation of bottlenecks in supply chain system for minimizing total cost by integrating manufacturing modelling based on MINLP approach. Applied Sciences (Switzerland), 9(6). https://doi.org/10.3390/app9061185

Ikegwu, A. C., Nweke, H. F., Anikwe, C. V., Alo, U. R., & Okonkwo, O. R. (2022). Big data analytics for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions. Cluster Computing, 25(5), 3343–3387. https://doi.org/10.1007/s10586-022-03568-5

Johan, A., & Soediantono, D. (2022). Literature Review of the Benefits of Lean Manufacturing on Industrial Performance and Proposed Applications in the Defense Industries. Journal of Industrial Engineering & Management Research, 3(2), 13–23. Retrieved from https://www.jiemar.org/index.php/jiemar/article/view/272

Judijanto, L., Pratama, I. W. A., Jata, I. W., & Utami, E. Y. (2024). Analisis Bibliometrik tentang Pengaruh Big Data dan Analitik dalam Pengembangan Produk dan Layanan. Jurnal Multidisiplin West Science, 3(01), 88–97. https://doi.org/10.58812/jmws.v3i01.942

Lazarova?Molnar, S., Mohamed, N., & Al?Jaroodi, J. (2019). Data analytics framework for Industry 4.0: enabling collaboration for added benefits. IET Collaborative Intelligent Manufacturing, 1(4), 117–125. https://doi.org/10.1049/iet-cim.2019.0012

Lutfi, A. M., & Sunardi, N. (2019). PENGARUH CURRENT RATIO (CR), RETURN ON EQUITY (ROE), DAN SALES GROWTH TERHADAP HARGA SAHAM YANG BERDAMPAK PADA KINERJA KEUANGAN PERUSAHAAN (Pada Perusahaan Manufaktur Sektor Makanan dan Minuman Yang terdaftar di Bursa Efek Indonesia). Jurnal SEKURITAS (Saham, Ekonomi, Keuangan Dan Investasi), 2(3), 83. https://doi.org/10.32493/skt.v2i3.2793

Permatasari, R., Rezha, A., Najaf, E., & Sisephaputra, B. (2022). Business Process Evaluation of ITS Medical Center using Value Stream Mapping. Nusantara Science and Technology Proceedings, 2022, 17–23. Galaxy Science. https://doi.org/10.11594/nstp.2022.2904

Ravishankar TK. (2022). Six Sigma Methodologies to Improve Processes in Industries. International Research Journal of Engineering and Technology, 891–895. Retrieved from www.irjet.net

Rojek, I., Jasiulewicz-Kaczmarek, M., Piechowski, M., & Miko?ajewski, D. (2023). An Artificial Intelligence Approach for Improving Maintenance to Supervise Machine Failures and Support Their Repair. Applied Sciences (Switzerland), 13(8). https://doi.org/10.3390/app13084971

Saptioratri Budiono, H. D., Nurcahyo, R., & Habiburrahman, M. (2021). Relationship between manufacturing complexity, strategy, and performance of manufacturing industries in Indonesia. Heliyon, 7(6), e07225. https://doi.org/10.1016/j.heliyon.2021.e07225

Saputra, D., Berry, Y., Hamali, S., Gaspersz, Vi., Syamil, A., Ubud, S., … Panudju, A. A. T. (2023). Manajemen Operasi: Inovasi, Peluang, dan Tantangan Ekonomi Kreatif di Indonesia. PT. Sonpedia Publishing Indonesia.

Soemohadiwidjojo, A. T. (2017). Six Sigma Metode Pengukuran Kinerja Perusahaan Berbasis Statistik. Raih Asa Sukses.

Suhendi, S., Hetharia, D., & Marie, I. A. (2019). Perancangan Model Lean Manufacturing Untuk Mereduksi Biaya Dan Meningkatkan Customer Perceived Value. Jurnal Ilmiah Teknik Industri, 6(1). https://doi.org/10.24912/jitiuntar.v6i1.3023

Sun, Z., Strang, K., & Li, R. (2018). Big data with ten big characteristics. ACM International Conference Proceeding Series, (October), 56–61. https://doi.org/10.1145/3291801.3291822

Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3(June), 100026. https://doi.org/10.1016/j.sca.2023.100026

Tokuç, A. A., Uran, Z. E., & Tekin, A. T. (2019). Management of Big Data Projects. In Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution (pp. 279–293). https://doi.org/10.4018/978-1-5225-7865-9.ch015

Waras, M., & Sulistyowati, W. (2021). Implementation of Lean Six Sigma in an Effort to Reduce the Failure of the Pipe Quality Load Test. Procedia of Engineering and Life Science, 1(2). https://doi.org/10.21070/pels.v1i2.933

Xu, J., Naseer, H., Maynard, S., & Filippou, J. (2021). Leveraging Data and Analytics for Digital Business Transformation through DataOps: An Information Processing Perspective. ACIS 2021 - Australasian Conference on Information Systems, Proceedings, 1–11.

Yaqub, M. Z., & Alsabban, A. (2023). Industry-4.0-Enabled Digital Transformation: Prospects, Instruments, Challenges, and Implications for Business Strategies. Sustainability (Switzerland), 15(11). https://doi.org/10.3390/su15118553

Zakaria, M., Muhamad, U., & Tamyiz, H. (2023). Perancangan Akuisisi Data Monitoring Kondisi Area Mesin Calendering Tekstil Dengan IoT. Progresif: Jurnal Ilmiah Komputer, 20(1), 1–12. Retrieved from http://ojs.stmik-banjarbaru.ac.id/index.php/progresif/article/view/1467

Zanezi, A. C., & de Carvalho, M. M. (2023). How project management principles affect Lean Six Sigma program and projects: a systematic literature review. Brazilian Journal of Operations and Production Management, 20(1). https://doi.org/10.14488/BJOPM.1564.2023

Downloads

ARTICLE Published HISTORY

Submitted Date: 2024-03-23
Accepted Date: 2024-03-24
Published Date: 2024-04-01

How to Cite

Aprijal, R., Siregar, I. W., Siahaan, A. P. U., & Marlina, L. (2024). Utilization of Data Analytics to Enhance Operational Efficiency in Manufacturing Companies. Journal of Computer Networks, Architecture and High Performance Computing, 6(2), 514-521. https://doi.org/10.47709/cnahpc.v6i2.3723